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Physarum Learner: A Slime Mold Inspired Structural Learning Approach

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Advances in Physarum Machines

Part of the book series: Emergence, Complexity and Computation ((ECC,volume 21))

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Abstract

A novel Score-based Physarum Learner algorithm for learning Bayesian Network structure from data is introduced and shown to outperform common score based structure learning algorithms for some benchmark data sets. The Score-based Physarum Learner first initializes a fully connected Physarum-Maze with random conductances. In each Physarum Solver iteration, the source and sink nodes are changed randomly, and the conductances are updated. Connections exceeding a predefined conductance threshold are considered as Bayesian Network edges, and the score of the connected nodes are examined in both directions. A positive or negative feedback is given to the edge conductance based on the calculated scores. Due to randomness in selecting connections for evaluation, an ensemble of Score-based Physarum Learner is used to build the final Bayesian Network structure.

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Authors and Affiliations

  1. CIML Lab, Department of Biophysics, University of Regensburg, Regensburg, Germany

    T. Schön, O. Belova, A. Koch & E. W. Lang

  2. Department of Bioinformatics, University of Applied Science Weihenstephan-Triesdorf, Freising, Germany

    M. Stetter

  3. IEETA, Department of Electrical Engineering, Telecommunications and Informatics, Universidade de Aveiro, Aveiro, Portugal

    A. M. Tomé

Authors
  1. T. Schön
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  2. M. Stetter
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  3. O. Belova
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  4. A. Koch
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  5. A. M. Tomé
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  6. E. W. Lang
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Correspondence to E. W. Lang .

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Editors and Affiliations

  1. Unconventional Computing Centre, University of the West of England, Bristol, United Kingdom

    Andrew Adamatzky

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Schön, T., Stetter, M., Belova, O., Koch, A., Tomé, A.M., Lang, E.W. (2016). Physarum Learner: A Slime Mold Inspired Structural Learning Approach. In: Adamatzky, A. (eds) Advances in Physarum Machines. Emergence, Complexity and Computation, vol 21. Springer, Cham. https://doi.org/10.1007/978-3-319-26662-6_25

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  • DOI: https://doi.org/10.1007/978-3-319-26662-6_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26661-9

  • Online ISBN: 978-3-319-26662-6

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